Skrandies W
Max-Planck-Institute for Physiological and Clinical Research, Bad Nauheim, FRG.
Brain Topogr. 1989 Fall-Winter;2(1-2):73-80. doi: 10.1007/BF01128845.
Electroencephalographic data recorded for topographical analysis constitute multidimensional observations, and the present paper illustrates methods of data analysis of multichannel recordings where components of evoked brain activity are identified quantitatively. The computation of potential field strength (Global Field Power, GFP) is used for component latency determination. Multivariate statistical methods like Principal Component Analysis (PCA) may be applied to the topographical distribution of potential values. The analysis of statistically defined components of visually elicited brain activity is illustrated with data sets stemming from different experiments. With spatial PCA the dimensionality of multichannel data is reduced to only three components that account for more than 90% of the variance. The results of spatial PCA relate to experimental conditions in a meaningful way, and this method may also be used for time segmentation of topographic potential maps series.
为进行地形分析而记录的脑电图数据构成多维观测值，本文阐述了多通道记录数据分析方法，其中诱发脑电活动成分可被定量识别。通过计算电位场强度（全局场功率，GFP）来确定成分潜伏期。多元统计方法如主成分分析（PCA）可应用于电位值的地形分布。本文通过来自不同实验的数据集来说明对视诱发脑电活动的统计定义成分的分析。通过空间PCA，多通道数据的维度被缩减至仅三个成分，这三个成分解释了超过90%的方差。空间PCA的结果以有意义的方式与实验条件相关，并且该方法也可用于地形电位图序列的时间分割。
